Real-world business decision models frequently contain both hard rules and soft rules that can be violated when necessary. Representing and maintaining such rules becomes even more challenging when they conflict with each other and new rules need to be added — at which point a traditional rule-based approach begins to break down.

With this in mind, I will present a different approach that allows subject matter experts to express business rules with a certain degree of probability. Traditional DMN-style decision tables can be extended by assigning a low, medium, or high probability to conflicting rules. A decision engine then determines the optimal decision by satisfying the most probable combination of all applicable business rules.